Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties

Currently, evolutionary multiobjective optimization (EMO) algorithms have been successfully used to find a good approximation of many-objective optimization problems (MaOPs). To measure the performance of EMO algorithms, many benchmark multiobjective test problems have been constructed. Among them,...

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Main Authors: Li, Hui, Deb, Kalyanmoy, Zhang, Qingfu, Suganthan, Ponnuthurai Nagaratnam, Chen, Lei
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/151704
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1517042021-07-09T08:16:27Z Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties Li, Hui Deb, Kalyanmoy Zhang, Qingfu Suganthan, Ponnuthurai Nagaratnam Chen, Lei School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Many-objective Optimization Evolutionary Algorithms Currently, evolutionary multiobjective optimization (EMO) algorithms have been successfully used to find a good approximation of many-objective optimization problems (MaOPs). To measure the performance of EMO algorithms, many benchmark multiobjective test problems have been constructed. Among them, DTLZ and WFG are two representative test suites with the scalability to the number of variables and objectives. It should be pointed out that MaOPs can be more challenging if they are involved with difficult problem features, such as objective scalability, complicated Pareto set, bias, disconnection, or degeneracy. In this paper, a set of ten new test problems with above-mentioned difficulties are constructed. Some experimental results on these test problems found by two popular EMO algorithms, i.e., MOEA/D and NSGA-III, are reported and analyzed. Moreover, the performance of these two EMO algorithms with different population sizes on these test problems are also studied. This work was supported by National Natural Science Foundation of China under Grant 61573279 , Grant 61175063 , Grant U1811461 , Grant 11690011 , and Grant 61721002 . This work was also supported by a grant from ANR/RGC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region , China and France National Research Agency (Project No. A-CityU101/16 ). 2021-07-09T08:16:27Z 2021-07-09T08:16:27Z 2019 Journal Article Li, H., Deb, K., Zhang, Q., Suganthan, P. N. & Chen, L. (2019). Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties. Swarm and Evolutionary Computation, 46, 104-117. https://dx.doi.org/10.1016/j.swevo.2019.02.003 2210-6502 https://hdl.handle.net/10356/151704 10.1016/j.swevo.2019.02.003 2-s2.0-85062232641 46 104 117 en Swarm and Evolutionary Computation © 2019 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Many-objective Optimization
Evolutionary Algorithms
spellingShingle Engineering::Electrical and electronic engineering
Many-objective Optimization
Evolutionary Algorithms
Li, Hui
Deb, Kalyanmoy
Zhang, Qingfu
Suganthan, Ponnuthurai Nagaratnam
Chen, Lei
Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
description Currently, evolutionary multiobjective optimization (EMO) algorithms have been successfully used to find a good approximation of many-objective optimization problems (MaOPs). To measure the performance of EMO algorithms, many benchmark multiobjective test problems have been constructed. Among them, DTLZ and WFG are two representative test suites with the scalability to the number of variables and objectives. It should be pointed out that MaOPs can be more challenging if they are involved with difficult problem features, such as objective scalability, complicated Pareto set, bias, disconnection, or degeneracy. In this paper, a set of ten new test problems with above-mentioned difficulties are constructed. Some experimental results on these test problems found by two popular EMO algorithms, i.e., MOEA/D and NSGA-III, are reported and analyzed. Moreover, the performance of these two EMO algorithms with different population sizes on these test problems are also studied.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Hui
Deb, Kalyanmoy
Zhang, Qingfu
Suganthan, Ponnuthurai Nagaratnam
Chen, Lei
format Article
author Li, Hui
Deb, Kalyanmoy
Zhang, Qingfu
Suganthan, Ponnuthurai Nagaratnam
Chen, Lei
author_sort Li, Hui
title Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
title_short Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
title_full Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
title_fullStr Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
title_full_unstemmed Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
title_sort comparison between moea/d and nsga-iii on a set of many and multi-objective benchmark problems with challenging difficulties
publishDate 2021
url https://hdl.handle.net/10356/151704
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